A securities trading mechanism can be thought of as a set of protocols that translate investors' latent demands into realized prices and quantities. Various automated trading systems are known, which execute so-called “program” trading strategies in response to market movements.
Traders for large institutional investors such as mutual funds, hedge funds, etc. face a dilemma. On the one hand, they are responsible for trading large blocks of stocks and cannot afford to send large market orders that will result in adverse market impact and inferior execution prices. On the other hand, they require a certain degree of immediacy to be able to complete their trades within a defined time horizon. It is therefore typical for such traders to utilize a trading strategy to complete execution of a trading list of a large number of different securities within a specified or given time frame, wherein multiple smaller orders for portions of the trading list are sent over the given time frame according to a predefined trading strategy model that minimizes the risk to the unexecuted portion of the trading list of unfavorable market movements caused by execution of the smaller orders.
One such known trading risk objective strategy treats the unexecuted trade list as a long-short portfolio and utilizes a multi-factor risk model to construct a minimal risk “portfolio” of unfilled orders to be sent simultaneously for execution. The “portfolio” of unfilled orders when executed minimizes the risk to short-term return of the unexecuted trade list.
The Markowitz Model (as described in “Portfolio Selection,” Dr. H. M. Markowitz, Journal of Finance, Mar. 7, 1952), is an optimization model that balances the expected return and risk of a portfolio to allow the construction of a minimal risk portfolio. The decision variables are the amounts invested in each asset. According to this model, the statistical variance a stock's price is used as a measure of its risk, the expected return of the stock is used as a measure of its utility or long-term prospects, and the variance of a portfolio's return is derived from the covariances for the returns of the individual assets in the portfolio.
Variance is a measure of the fluctuation in the rate of return, where higher variances indicate riskier investments, while covariance is a measure of the correlation of return fluctuations of one asset (e.g., stock) with the return fluctuations of another. A high covariance between two stocks indicates that an increase in one stock's return is likely to correspond to an increase in the other stock's return, a low covariance indicates that the return rates of the two stocks are relatively independent, and a negative covariance indicates that an increase in one stock's return is likely to correspond to a decrease in the other stock's return. Thus, the risk of a portfolio is not determined by a simple weighted average of the risks of individual assets in the portfolio, but instead is determined by the mutual relation between the returns of individual assets in the portfolio.
A shortcoming of the known trading risk objective model is that it fails to take into account the short-term effect that each trade has on the overall portfolio of holdings, which includes securities held but not traded as well as the unexecuted trade list of securities. Because portfolio managers are evaluated on the variance of their holdings' return, the actions of a trader using the known trading risk model may have an adverse effect on the short-term risk/return of the overall portfolio of holdings, since the trader is unaware of the portfolio's untraded holdings. Here, it is assumed that there is little coupling between the long-term utility of the portfolio upon which the trade list is based, and the short-term utility that can be maximized using the trade strategy.
There thus exists a need for improvements in the art to provide for control of the short-term risk of the overall holdings of a portfolio upon which a trade list is based.